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MIM-Reasoner: Learning with Theoretical Guarantees for Multiplex Influence Maximization (2402.16898v2)

Published 24 Feb 2024 in cs.SI, cs.AI, cs.LG, math.PR, and stat.ML

Abstract: Multiplex influence maximization (MIM) asks us to identify a set of seed users such as to maximize the expected number of influenced users in a multiplex network. MIM has been one of central research topics, especially in nowadays social networking landscape where users participate in multiple online social networks (OSNs) and their influences can propagate among several OSNs simultaneously. Although there exist a couple combinatorial algorithms to MIM, learning-based solutions have been desired due to its generalization ability to heterogeneous networks and their diversified propagation characteristics. In this paper, we introduce MIM-Reasoner, coupling reinforcement learning with probabilistic graphical model, which effectively captures the complex propagation process within and between layers of a given multiplex network, thereby tackling the most challenging problem in MIM. We establish a theoretical guarantee for MIM-Reasoner as well as conduct extensive analyses on both synthetic and real-world datasets to validate our MIM-Reasoner's performance.

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References (42)
  1. Boosting reinforcement learning in competitive influence maximization with transfer learning. In 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI), pages 395–400. IEEE, 2018.
  2. A survey on influence maximization in a social network. KAIS, 62(9):3417–3455, 2020.
  3. Approximate algorithms for some generalized knapsack problems. Theoretical Computer Science, 3(3):293–304, 1976.
  4. Touplegdd: A fine-designed solution of influence maximization by deep reinforcement learning. arXiv preprint arXiv:2210.07500, 2022.
  5. Scalable influence maximization for prevalent viral marketing in large-scale social networks. In Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’10, page 1029–1038, New York, NY, USA, 2010a. Association for Computing Machinery. ISBN 9781450300551. doi: 10.1145/1835804.1835934. URL https://doi.org/10.1145/1835804.1835934.
  6. Scalable influence maximization in social networks under the linear threshold model. In 2010 IEEE International Conference on Data Mining, pages 88–97, 2010b. doi: 10.1109/ICDM.2010.118.
  7. Deep graph representation learning influence maximization with accelerated inference. Available at SSRN 4663083, 2024.
  8. Pearson correlation coefficient. Noise reduction in speech processing, pages 1–4, 2009.
  9. Discriminative embeddings of latent variable models for structured data. International Conference on Machine Learning, 2016.
  10. Structural reducibility of multilayer networks. Nature communications, 6(1):6864, 2015.
  11. Unraveling the origin of social bursts in collective attention. Scientific Reports, 2020. doi: 10.1038/s41598-020-61523-z. URL https://doi.org/10.1038/s41598-020-61523-z.
  12. Celf++: Optimizing the greedy algorithm for influence maximization in social networks. volume 47-48, pages 47–48, 03 2011. doi: 10.1145/1963192.1963217.
  13. Influence maximization revisited: Efficient reverse reachable set generation with bound tightened. In Proc. of the SIGMOD, pages 2167–2181, 2020.
  14. Simulated annealing based influence maximization in social networks. In Proc. of the AAAi, 2011.
  15. Cim: clique-based heuristic for finding influential nodes in multilayer networks. Applied Intelligence, 52(5):5173–5184, 2022.
  16. Maximizing the spread of influence through a social network. In Proc. of the KDD, 2003.
  17. M. Kimura and K. Saito. Tractable models for information diffusion in social networks. In J. Fürnkranz, T. Scheffer, and M. Spiliopoulou, editors, Knowledge Discovery in Databases: PKDD 2006, pages 259–271, Berlin, Heidelberg, 2006. Springer Berlin Heidelberg. ISBN 978-3-540-46048-0.
  18. D. Koller and N. Friedman. Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning. The MIT Press, 2009. ISBN 0262013193.
  19. Multiplex influence maximization in online social networks with heterogeneous diffusion models. IEEE Transactions on Computational Social Systems, 5(2):418–429, 2018.
  20. Cost-effective outbreak detection in networks. In Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 420–429, 2007.
  21. Piano: Influence maximization meets deep reinforcement learning. IEEE Transactions on Computational Social Systems, 2022.
  22. Tiptop: (almost) exact solutions for influence maximization in billion-scale networks. IEEE/ACM Transactions on Networking, 27:649–661, 2019. doi: 10.1109/TNET.2019.2898413.
  23. Influence maximization on social graphs: A survey. TKDE, 30(10):1852–1872, 2018.
  24. A survey on influence maximization: From an ml-based combinatorial optimization. ACM Transactions on Knowledge Discovery from Data, 17(9):1–50, 2023.
  25. The cross-platform synergies of digital video advertising: Implications for cross-media campaigns in television, internet and mobile tv. Computers in Human Behavior, 48:463–472, 2015.
  26. A learning-based framework to handle multi-round multi-party influence maximization on social networks. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 695–704, 2015.
  27. Deepgar: Deep graph learning for analogical reasoning. In 2022 IEEE International Conference on Data Mining, pages 1065–1070, 2022a.
  28. Source localization of graph diffusion via variational autoencoders for graph inverse problems. In Proc. of the KDD, 2022b.
  29. Deep graph representation learning and optimization for influence maximization. In International Conference on Machine Learning, pages 21350–21361. PMLR, 2023.
  30. Gcomb: Learning budget-constrained combinatorial algorithms over billion-sized graphs. Advances in Neural Information Processing Systems, 33:20000–20011, 2020.
  31. Least cost influence in multiplex social networks: Model representation and analysis. In 2013 IEEE 13th International Conference on Data Mining, pages 567–576, 2013. doi: 10.1109/ICDM.2013.24.
  32. Stop-and-stare: Optimal sampling algorithms for viral marketing in billion-scale networks. In Proc. of the SIGMOD, 2016.
  33. Interest-matching information propagation in multiple online social networks. ACM International Conference Proceeding Series, 10 2012. doi: 10.1145/2396761.2398525.
  34. Mim2: Multiple influence maximization across multiple social networks. Physica A: Statistical Mechanics and its Applications, 526:120902, 2019.
  35. Biogrid: A general repository for interaction datasets. Nucleic acids research, 34:D535–9, 01 2006. doi: 10.1093/nar/gkj109.
  36. Online processing algorithms for influence maximization. In Proc. of the SIGMOD, pages 991–1005, 2018.
  37. Influence maximization: Near-optimal time complexity meets practical efficiency. In Proc. of the SIGMOD, pages 75–86, 2014.
  38. Influence maximization in near-linear time: A martingale approach. In Proc. of the SIGMOD, 2015.
  39. Influence maximization across partially aligned heterogenous social networks. In Pacific-Asia conference on knowledge discovery and data mining, pages 58–69. Springer, 2015.
  40. Least cost influence maximization across multiple social networks. IEEE/ACM Transactions on Networking, 24(2):929–939, apr 2016. doi: 10.1109/tnet.2015.2394793.
  41. On the upper bounds of spread for greedy algorithms in social network influence maximization. IEEE Transactions on Knowledge and Data Engineering, 27(10):2770–2783, 2015. doi: 10.1109/TKDE.2015.2419659.
  42. Graph neural networks: A review of methods and applications. AI Open, 2018. doi: 10.1016/J.AIOPEN.2021.01.001.
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